Spotting Prior Claims and Open Litigation in Submission Files Using AI — A Practical Guide for the Risk Selection Analyst

Risk selection lives or dies on the quality of information extracted from submission files. Yet for Risk Selection Analysts, critical details about prior claims and open litigation are often buried across sprawling broker submission packages, inconsistent loss run reports, and loosely formatted litigation summaries. The result is slower quotes, missed red flags, and uneven underwriting outcomes. This article shows how Nomad Data’s Doc Chat changes that equation by delivering an AI review for open litigation in submissions and automating prior claim detection across entire submission packets in minutes, not days.

Doc Chat is a suite of purpose-built, AI-powered agents that can ingest broker submission packages, extract and normalize loss histories, and surface active lawsuits and litigation trends—no matter where they are buried in the files. With real-time Q&A, you can ask, “List all open suits by jurisdiction and the alleged cause,” or “Roll up all slip-and-fall claims by policy year,” and get instant, source-linked answers. For underwriting teams and Risk Selection Analysts in General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine, Doc Chat provides a repeatable, auditable way to find the facts that drive triage, appetite fit, and pricing confidence.

Why Prior Claims and Open Litigation Are So Hard to Find

On paper, submissions should make it easy: a clean application, current loss run reports, and a concise litigation summary. In reality, General Liability & Construction, Property & Homeowners, and Specialty & Marine accounts arrive as a patchwork of documents from multiple systems and prior carriers—each with different naming conventions, policy periods, and entity names (legal names, DBAs, predecessor entities, joint ventures, and project-specific LLCs). Open litigation is frequently only referenced in narrative form or appears as a one-line note in a broker email attachment. Loss runs may be partial, cutoff mid-year, or mixing paid, ALAE, and outstanding in inconsistent columns. That’s before you factor in acquisitions, name changes, or multi-jurisdiction operations.

For a Risk Selection Analyst, the nuance isn’t just “Are there losses?” but:

• Do losses cluster around specific operations, project types, or jurisdictions (e.g., New York Labor Law, Florida premises liability, California wildfire)?
• Are there open litigations that signal chronic hazards, habitual claimants, or defense-intensive venues?
• Do loss runs include related entities, DBAs, and prior owners, or are you seeing only the best slice?
• Are reserves realistic? Are “closed” claims reopened? Are demand letters escalating into formal filings?

When these answers are scattered across a 200–1,500 page submission, the path to a confident decline or a disciplined quote is anything but linear.

Line-of-Business Nuances That Complicate Diligence

General Liability & Construction

Risk Selection Analysts supporting GL & Construction grapple with multi-tier vendor relationships, additional insured endorsements, hold-harmless/indemnity provisions, and wrap-up programs (OCIP/CCIP). Prior claims may span bodily injury (BI), products/completed operations, third-party property damage, or contractor-caused incidents. Open litigation risk often hides in:

• Project lists and job cost reports that mention incidents without formal loss details
• Subcontractor agreements and certificates of insurance where gaps foreshadow downstream exposure
• OSHA 300/300A logs and citations indicating safety issues not reflected in loss runs
• Litigation summaries naming plaintiffs or venues but not tying to policy years or insured entities

Construction’s entity sprawl (JVs, project LLCs, DBAs) makes entity matching and cross-document normalization critical to avoid blind spots in prior claims detection automation underwriting.

Property & Homeowners

Property submissions include SOVs, COPE data, valuation/appraisal reports, engineering inspections, and catastrophe modeling outputs. Prior claims analysis must reconcile water damage, hail, wind, fire, and theft incidents with roof age, protective safeguards, and construction type. Open litigation might stem from bad-faith allegations, adjuster disputes, or subrogation conflicts buried in correspondence or broker narratives. Loss runs often mix catastrophe and non-cat losses; aligning them with locations and protective classes across time is non-trivial.

Specialty Lines & Marine

Marine and specialty accounts layer in vessel surveys, maintenance logs, P&I (Protection & Indemnity) claims histories, charter party agreements, and international jurisdictions. Open litigation may sit in maritime dockets or be summarized as correspondence from P&I clubs. Prior incidents (cargo damage, collision, personal injury to crew or passengers, pollution) are sometimes referenced in survey notes rather than formal loss runs. Entity names can differ across registries, flag states, and operating companies.

The Manual Workflow Today: Slow, Inconsistent, and Risky

Most underwriting shops still handle this manually. A Risk Selection Analyst prints or downloads the broker submission, scans ACORD forms (125/126/140), reads the statement of values (SOV) and COPE data, verifies protective safeguards, and then combs through loss run reports and litigation summaries. If something looks off, the analyst emails the broker for clarification, searches public dockets, checks internal notes from previous submissions, and adds findings to a spreadsheet.

This manual path is slow and error-prone:

• Loss runs differ by carrier and TPA formatting; column headers and definitions aren’t standardized.
• Open litigation can be referenced in emails or demand letters, not just the “Litigation Summary.”
• Related entities, DBAs, and project LLCs complicate rollups; people rely on memory or ad-hoc notes.
• Analysts spend hours on data entry—retyping claims into grids for trend analysis.
• During busy seasons, shallow reads miss exclusions, endorsements, or late-notice claims.

The consequences are familiar: slow cycle times, inconsistent risk decisions, leakage from mispriced business, and friction with brokers who want crisp answers quickly.

Where the Signals Hide in Submission Files

Across General Liability & Construction, Property & Homeowners, and Specialty & Marine, signals for prior claims and open litigation appear in more places than just the loss run:

• Broker submission packages: ACORD 125/126/140, narratives of operations, project lists, subcontractor schedules, hold-harmless/indemnity clauses, wrap-up documentation (OCIP/CCIP), COIs, and Additional Insured endorsements.
• Loss run reports: Multi-carrier PDFs with mixed policy periods, paid vs. outstanding vs. ALAE inconsistencies, reopened claims, subrogation recoveries, and partial-year coverage.
• Litigation summaries: Venue, cause of action, plaintiff counsel, defense counsel, matter status, but sometimes detached from policy years or insured names.
• Safety and inspection outputs: OSHA 300/300A logs, inspection reports, and recommendations that hint at unreported incidents.
• Marine and specialty artifacts: Vessel surveys, maintenance logs, P&I claims histories, charter party agreements, and surveyor comments that imply incidents.
• Broker correspondence and demand packages: Demand letters, service-of-process pages, or attorney communications that preface litigation not yet in the loss run.

Finding, normalizing, and connecting those breadcrumbs across a 400-page PDF is precisely where human review breaks down at scale.

AI Review for Open Litigation in Submissions: How Doc Chat Helps

Doc Chat reads every page of the submission packet, then answers questions the way your best Risk Selection Analyst would—only faster and more consistently. You can run an AI review for open litigation in submissions by uploading all broker materials: the ACORDs, the statement of values, COPE details, loss run reports, litigation summaries, inspection reports, and email addenda. Doc Chat then:

• Indexes and cross-references names and entities: Maps legal names, DBAs, predecessor entities, project LLCs, and related companies across documents.
• Normalizes loss data: Extracts claim dates, cause, location, paid/indemnity/ALAE, reserves, status (open/closed/reopened), and aligns them to consistent definitions.
• Surfaces litigation signals: Identifies docket references, plaintiff names, alleged causes, venues, counsel, and matter statuses, even when mentioned in narratives or email attachments.
• Builds timelines and rollups: Summarizes claims and lawsuits by policy year, location, jurisdiction, and cause of loss; flags clusters and defense-intensive venues.
• Links to source pages: Every extracted fact is cited back to the precise page/paragraph for instant verification.

Because Doc Chat uses your underwriting playbook, it scores what matters to your team—venue risk (e.g., NY Labor Law), repeated slip-and-fall patterns, sprinkler impairments tied to water damage claims, or vessel types linked to P&I frequency.

For a deeper understanding of why document inference (not just extraction) matters, see Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Prior Claims Detection Automation for Underwriting: Doc Chat’s Process

The phrase “prior claims detection automation underwriting” usually conjures images of simple table scraping from loss runs. Doc Chat goes further by replicating the cognitive steps a Risk Selection Analyst performs:

1. Entity resolution and alias mapping: Unifies “ABC Construction, LLC,” “ABC Construction Co.,” “ABC Project JV,” and “ABC Builders DBA” to avoid undercounting prior incidents.
2. Document-type awareness: Recognizes ACORDs, SOVs, COPE sheets, loss runs, litigation summaries, inspection reports, and marine surveys—extracting the right fields for each.
3. Cross-document inference: Connects a litigation mention in a narrative to a related claim in the loss run, even when dates or naming conventions differ.
4. Cause/jurisdiction normalization: Standardizes causes of loss and maps venues to defense intensity parameters from your playbook.
5. Portfolio-aware flags: Optional rules highlight exposures your carrier avoids or prices carefully (e.g., residential construction in certain states, unprotected frame construction, high-severity maritime venues).

When done, Doc Chat produces a concise summary—plus an interactive Q&A experience. Ask follow-ups like: “Which open litigations reference construction defects?” or “Show all slip-and-fall claims at locations with waxed floors and no mats.” Answers arrive with citations you can trust.

What This Looks Like in the Analyst’s Day-to-Day

With Doc Chat, the Risk Selection Analyst starts by dragging-and-dropping the submission packet. Within minutes, Doc Chat returns:

• Prior claims rollup: A standardized grid detailing date of loss, cause, paid, ALAE, outstanding, status, location, and jurisdiction—aligned across carriers and years.
• Open litigation map: Matters grouped by venue with alleged causes, plaintiffs, and defense counsel; links to source pages.
• Entity coverage: Confirmation that loss runs address all related entities, or flags for missing DBAs/JVs.
• Red flags: Reopened claims, repeated hazards (e.g., staircase falls), or venues that require rate/terms adjustments per your guidelines.
• Suggested broker questions: Automatically generated clarifications and document requests based on gaps (e.g., missing loss years, incomplete litigation status, absent SOV pages).

Instead of spending hours compiling data, the analyst begins at decision-making—triage, appetite fit, and pricing strategy—supported by clear, defensible evidence.

Sample Prompts Risk Selection Analysts Use

Real-time Q&A is where Doc Chat shines. Try prompts like:

• “List all open suits by jurisdiction, with alleged cause and plaintiff counsel; cite pages.”
• “Roll up all third-party BI claims by policy year with paid, ALAE, and outstanding; include reopened flags.”
• “Identify any construction defect allegations tied to residential projects in New York.”
• “Summarize water damage claims by building, with protective safeguards and sprinkler impairments noted.”
• “For vessels over 500 GT, list all P&I incidents by type and status, with links to survey notes.”
• “Which entities in this packet lack corresponding loss runs or litigation disclosure?”

Each answer includes page-level citations, so you can validate the facts in seconds.

Business Impact: Time, Cost, Accuracy, and Speed-to-Quote

Doc Chat’s impact compounds across Risk Selection Analyst workflows:

• Cycle time: Review entire submission packets in minutes instead of hours, shaving days off quote turnaround.
• Accuracy: Eliminate blind spots from inconsistent formats; standardize calculations and definitions across carriers and years.
• Cost: Reduce manual data entry and repeated broker back-and-forth; scale to peak volumes without overtime or new hires.
• Defensibility: Page-level citations provide a clear audit trail for underwriting committees, reinsurers, and regulators.

Carriers that automate prior claims detection and run an AI review for open litigation in submissions see more consistent appetite decisions, improved price adequacy, and better hit ratios—because quotes arrive faster and with fewer conditionalities. For a sense of the magnitude possible when AI removes bottlenecks in document-heavy reviews, see Great American Insurance Group Accelerates Complex Claims with AI and AI’s Untapped Goldmine: Automating Data Entry.

How Doc Chat Works Under the Hood

Doc Chat is not a generic summarizer. It is trained on your underwriting playbooks, document types, and standards so that outputs mirror your team’s expectations. Key capabilities include:

• Volume: Ingests entire submission files—thousands of pages at a time—without adding headcount.
• Complexity: Finds exclusions, endorsements, and trigger language hidden in dense policies and contracts that impact claims likelihood.
• Thoroughness: Surfaces every reference to coverage, liability, or damages—minimizing leakage and disputes downstream.
• Real-time Q&A: Ask natural-language questions across the entire packet and receive instant, source-linked answers.
• Custom outputs: Structured grids, risk memos, appetite assessments, and broker request lists in your formats.

Nomad’s team partners with you to encode the unwritten expertise that drives your best risk selection decisions. That’s essential because, as we discuss in Beyond Extraction, the rules that matter most often aren’t written down—they live in expert judgment. Doc Chat captures and standardizes that knowledge so every analyst benefits, day one.

Security, Compliance, and Auditability

Underwriting involves sensitive data and rigorous oversight. Doc Chat supports strict data governance with enterprise-grade security and clear document-level traceability for every answer. The platform is built for regulated environments and provides the auditability needed to satisfy internal compliance, external regulators, and reinsurers. For more on how transparent, page-linked reasoning builds trust in high-stakes insurance workflows, see the GAIG case study.

Why Nomad Data: A Partner, Not Just a Product

Many underwriters have tried one-size-fits-all tools that summarize but don’t understand underwriting nuance. Nomad Data’s Doc Chat is different:

• White-glove onboarding: We interview your Risk Selection Analysts to capture playbooks and edge cases, then configure Doc Chat accordingly.
• Rapid time to value: Most teams are live in 1–2 weeks. You can start with simple drag-and-drop uploads and add system integrations later.
• Co-creation: We iterate with your team to refine prompts, presets, and outputs so Doc Chat feels like a seasoned analyst on day one.
• Scales with you: Handle seasonal submission surges without staffing spikes. Expand from GL & Construction into Property & Homeowners and Specialty & Marine with line-specific presets.

Explore the full product overview here: Doc Chat for Insurance.

Illustrative Scenario: From 2 Days to 20 Minutes

A broker submits a 600-page package for a mid-market contractor with mixed commercial and residential projects across three states. The packet includes ACORDs, five years of multi-carrier loss runs, a litigation summary, OSHA logs, subcontractor agreements, and scattered email notes. Manually, a Risk Selection Analyst spends 8–10 hours assembling a claims rollup, reconciling open matters, and drafting broker questions.

With Doc Chat, the analyst drops in the PDF bundle and asks:

• “Summarize all open litigation by venue and cause of action; include plaintiff counsel and status.”
• “Normalize loss runs for the last five years and roll up paid/ALAE/outstanding by policy year.”
• “Highlight any claims reopened in the last 24 months and link to relevant pages.”
• “List entities referenced in the submission and confirm which have associated loss runs.”
• “Draft broker questions for any missing years, partial runs, or incomplete litigation data.”

In under 20 minutes, Doc Chat returns a page-linked litigation summary, a standardized claims grid, missing-entity alerts, and a broker-ready RFI list. The analyst proceeds directly to triage and pricing strategy, with a defensible record of what the file said—and didn’t say.

Going Beyond Extraction: Institutionalizing Expertise

The biggest underwriting advantage isn’t just speed. It’s consistency. Doc Chat captures the “unwritten rules” your top analysts use to recognize patterns—like the venues that drive defense costs, or the inspection note that implies an unreported leak history—and makes them repeatable across the team. We’ve written about this shift from ad-hoc expertise to codified process in Beyond Extraction, and why it fuels sustained operating leverage.

Frequently Asked Questions from Risk Selection Analysts

Does Doc Chat work only with loss runs?

No. It reads the entire submission, including ACORD forms, SOVs, COPE sheets, litigation summaries, OSHA logs, marine surveys, subcontractor agreements, additional insured endorsements, and broker emails. It extracts and cross-references signals across every page.

Can Doc Chat validate information against external sources?

Yes—when you choose to connect approved third-party data sources or internal repositories. Doc Chat can enrich and verify details to the extent your data governance allows, consistent with the approach discussed in The End of Medical File Review Bottlenecks.

What about auditability and trust?

Every answer includes citations back to the exact page and paragraph. Oversight teams can quickly confirm what Doc Chat found, supporting underwriting committees, reinsurers, and regulators. This page-level transparency underpins the trust highlighted in the GAIG webinar recap.

How long does it take to get started?

Most teams are live within 1–2 weeks. You can begin with a proof-of-value using drag-and-drop uploads, then add workflow integrations via modern APIs as adoption grows. For a broader view of implementation and change management, see AI for Insurance: Real-World Use Cases.

Implementation: White-Glove, 1–2 Weeks to Value

Nomad’s delivery model is designed for speed without sacrificing precision:

1. Discovery: We review example submissions from General Liability & Construction, Property & Homeowners, and Specialty & Marine and capture your risk selection criteria, preferred outputs, and “watchlist” venues/causes.
2. Preset design: We configure Doc Chat presets for your standard outputs—loss rollups, litigation maps, appetite checks, and broker question lists.
3. Pilot: Your Risk Selection Analysts run live submissions; we tune prompts and rules to your feedback.
4. Scale-up: Optional integration to intake portals, document repositories, and underwriting workbenches; set up portfolio-wide reviews during peak season.

Because Doc Chat is fit to your documents and playbooks, adoption is quick and intuitive. Analysts see immediate time savings and improved consistency in how prior claims and open litigation are surfaced.

A Playbook for AI-Ready Risk Selection

To capitalize on AI review for open litigation in submissions and prior claims detection automation in underwriting, consider these steps:

• Start with the bottleneck: Target high-volume lines (e.g., GL & Construction) where loss runs and litigation are most variable.
• Define the must-haves: Agree on the critical fields for loss normalization, venue risk parameters, and appetite triggers.
• Standardize outputs: Use Doc Chat presets to enforce consistent grids and memos across analysts and lines.
• Institutionalize feedback: Feed edge cases back into Doc Chat to improve performance—your playbook gets stronger with each submission.

This approach reflects lessons we’ve seen across carriers adopting document intelligence at scale, outlined in Reimagining Claims Processing Through AI Transformation and AI for Insurance: Real-World Use Cases.

Conclusion: Turn Every Page into an Advantage

For Risk Selection Analysts, the question is no longer whether AI can read a submission—it’s whether it can surface precisely the prior claims and open litigation signals that determine appetite, pricing, and terms. Nomad Data’s Doc Chat does exactly that, turning broker submission packages into structured, verifiable intelligence in minutes. The payoff is faster, fairer, and more defensible underwriting across General Liability & Construction, Property & Homeowners, and Specialty Lines & Marine.

Ready to see how quickly you can transform prior claims detection and litigation review? Learn more and request a tailored walkthrough: Doc Chat for Insurance.